Pregnancy dysglycemia was associated with a higher estimated risk of metabolic dysfunction–associated steatotic liver disease using cardiometabolic risk factors measured 10–14 years after delivery.
Abstract
OBJECTIVE:
To determine whether pregnancy dysglycemia in the early third trimester was associated with a higher estimated risk of postpartum metabolic dysfunction–associated steatotic liver disease (MASLD) assessed 10–14 years after delivery.
METHODS:
This was a secondary analysis from the prospective international cohort study HAPO FUS (Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study). The exposure was pregnancy dysglycemia in the third trimester, identified by the glucose summary z-score of a 75-g oral glucose tolerance test (OGTT) at 24–32 weeks of gestation, and, secondarily, a diagnosis of gestational diabetes mellitus (GDM). The outcome was the estimated 6-year risk of MASLD determined by the Framingham Fatty Liver Disease Risk Function. Multivariable linear and multinomial regression models were used and adjusted for baseline covariates at the time of the OGTT, including study field center, age, parity, gestational age, and duration from enrollment to follow-up.
RESULTS:
Of 4,697 assessed pregnant individuals, the median age was 30.5 years (interquartile range 26.0, 34.1 years) and the median body mass index was 26.6 (interquartile range 24.1, 29.9); 14.3% (n=672) were diagnosed with GDM. At 10–14 years after delivery (median 11.6 years, interquartile range 10.8, 12.4 years), the median estimated 6-year risk of MASLD was 6.0% (interquartile 3.0%, 13.0%). Individuals with a higher third trimester glucose summary z-score were at higher estimated risk of postpartum MASLD (adjusted beta coefficient 3.2 per 1 SD; 95% CI, 2.7–3.6), as were those who were diagnosed with GDM (adjusted beta coefficient 4.2%; 95% CI, 3.1–5.2). When MASLD risk was assessed in tertiles, an increase of 1 SD in the summary z-score was associated with greater adjusted odds of being in the highest-risk MASLD tertile (adjusted odds ratio 1.1; 95% CI, 1.0–1.3); however, a diagnosis of GDM was not associated with greater adjusted odds of being in the highest-risk MASLD tertile.
CONCLUSION:
Increasing pregnancy dysglycemia in the third trimester was associated with a higher estimated risk of postpartum MASLD using cardiometabolic risk factors measured 10–14 years after delivery. Whether postpartum interventions to improve cardiometabolic health for individuals with prior pregnancy dysglycemia decreases the risk of MASLD requires further investigation.
Metabolic dysfunction–associated steatotic liver disease (MASLD) is one of the most frequent chronic liver conditions and affects approximately 30% of the population globally.1,2 Complications of MASLD include hepatic decompensation, malignancy, and MASLD cirrhosis-related death.2–4 Cirrhosis related to MASLD is the primary cause of liver transplantation among female individuals.2,5,6 Individuals with MASLD are at an increased risk of developing extrahepatic cardiometabolic complications such as diabetes, cardiovascular disease, and chronic kidney disease and have a higher all-cause mortality rate.1,7 In addition, there is a strong and bidirectional association between diabetes and MASLD given the common pathophysiologic role of insulin resistance.8,9
Gestational diabetes mellitus (GDM) is the most frequent metabolic adverse pregnancy outcome, affects more than 8% of pregnancies, and increases the lifetime risk of diabetes by more than 10-fold.10–12 Prior studies have focused on cardiometabolic risk factors for MASLD such as obesity, diabetes, disorders of cholesterol metabolism, and hypertension in nonpregnant individuals.9,13–15 The extent to which pregnancy dysglycemia, including but not limited to a clinical diagnosis of GDM, may affect the risk of developing MASLD in the postpartum period remains to be fully defined.16,17 Given that cardiometabolic risk factors are potentially modifiable and are generally measured as part of routine clinical care, identifying individuals in the peripartum period with dysglycemia who are at high risk for MASLD could inform future interventions aimed at improving maternal cardiovascular health.
Diagnosis of MASLD can be challenging because liver biopsy is the gold standard for diagnosis and advanced imaging procedures can be difficult to implement across clinical settings.18,19 The Framingham Fatty Liver Disease Risk Function is a validated instrument among individuals of reproductive age that can be used to determine MASLD risk using cardiometabolic factors that are generally available as part of routine clinical care.20,21 The risk derived from this model has yet to be examined in association with pregnancy dysglycemia in the postpartum period.
The objective of the analysis was to determine whether pregnancy dysglycemia in the early third trimester was associated with higher estimated risk of MASLD, as measured 10–14 years after delivery by the Framingham Fatty Liver Disease Risk Function. We hypothesized that greater pregnancy dysglycemia would be associated with a greater estimated risk of postpartum MASLD.
METHODS
HAPO FUS (Hyperglycemia and Adverse Pregnancy Outcome Follow-Up Study) was a prospective international cohort study conducted between 2013 and 2016. Briefly, the original HAPO study was a population-based cohort designed to examine the relationship between pregnancy dysglycemia and adverse pregnancy outcomes.22 Participants and clinicians were blinded to pregnancy dysglycemia screening results; hence, participants did not receive treatment for GDM. The HAPO FUS was a follow-up of the HAPO cohort designed to examine the relationship between pregnancy dysglycemia and cardiometabolic health 10–14 years after delivery.23,24 For the HAPO FUS, 10 of 15 HAPO sites located in Barbados, Canada, Hong Kong, Israel, Thailand, the United Kingdom, and the United States were included based on their ability to recruit and retain participants. Briefly, participants from sites that participated in the HAPO FUS also were excluded if they delivered before 37 weeks of gestation in the HAPO study, major fetal malformations, or neonatal death.22 Additionally, individuals with preexisting diabetes mellitus at the time of mid-pregnancy enrollment were excluded from enrollment in the HAPO study, per the original study protocol. The study protocols for both HAPO and HAPO FUS were approved by each participating site's institutional review board. Study participants provided written informed consent before participation.
The primary exposure for this analysis was the sum of glucose z-scores across the three timepoints (ie, fasting, 1 hour, and 2 hours) of a 75-g oral glucose tolerance test (OGTT) at 24–32 weeks of gestation. Secondarily, to facilitate clinical interpretation, we examined whether a participant had sufficient dysglycemia to result in a clinical diagnosis of GDM (yes or no) per International Association of the Diabetes and Pregnancy Study Group criteria.25
The primary outcome for this analysis was the estimated risk of MASLD as determined by the Framingham Fatty Liver Disease Risk Function, a validated estimator of the risk of developing MASLD, based on diagnosis by multidetector-computed tomography over the subsequent 6-year time period (C statistic, 0.791).20,21 Of note, multidetector-computed tomography is a validated method for MASLD diagnosis based on liver biopsy.26 The outcome was modeled as a continuous measure to examine the continuous relationship between pregnancy dysglycemia and the estimated risk of hepatic steatosis. In addition, for ease of interpretation, we assessed the outcome categorically in tertiles from lowest (tertile 1) to highest estimated risk (tertile 3).
Briefly, the estimated risk was derived from a multivariable function (algorithm) that used cardiometabolic risk factors to calculate an estimate of developing MASLD over the subsequent 6 years. This model included the following variables: age (continuous), sex (male or female), body mass index (BMI, calculated as weight in kilograms divided by height in meters squared, continuous), alcohol use (yes or no), and fasting triglycerides (mg/dL, continuous); these variables are associated with hepatic steatosis in individuals at a mean age of 45 years.20 This model has been shown to have good-to-excellent discrimination, with an area under the curve of 0.79.20 The Framingham Fatty Liver Disease Risk Function is available online at: https://www.framinghamheartstudy.org/fhs-risk-functions/fatty-liver-disease/.
We used multivariable linear regression to examine the association between pregnancy dysglycemia (per 1 SD) and MASLD risk, which was modeled as a continuous measure of absolute risk (%), and calculated the beta coefficient with 95% CIs. We used multinomial logistic regression models to examine the association between pregnancy dysglycemia and the estimated odds of MASLD, with the low risk tertile as the referent, and calculated adjusted odds ratios (aORs) with 95% CIs. Covariates were selected for inclusion in the multivariable models based on a directed acyclic graph informed by theoretical paradigms of the relationship between among pregnancy outcomes,27 cardiometabolic risk factors,28 and MASLD risk. The final model adjusted for the baseline covariates assessed at HAPO enrollment (study field center [categorical], age [years, continuous], parity [continuous], and gestational age at OGTT [weeks, continuous]), as well as duration from enrollment to follow-up (years, continuous). We did not adjust for cardiometabolic conditions (ie, hypertension, diabetes, obesity, elevated lipids) because these factors were considered to be on the causal pathway between pregnancy dysglycemia and some cardiometabolic risk factors included in the prediction model.29,30 Given that the outcome was continuous, positive, and right-skewed, a gamma regression model with a log link function also was performed as a sensitivity analysis.
All statistical analyses were performed using R 4.2.0. P<.05 based on a two-tailed test was considered statistically significant.
RESULTS
Among 4,697 individuals enrolled in the HAPO FUS, the median age was 30.5 years (interquartile range 26.0 years, 34.1 years) and the median gestational age was 27.9 weeks (interquartile range 26.6 weeks, 28.9 weeks) at enrollment. Median BMI was 26.6 (interquartile range 24.1, 29.9), and 51.6% of the participants were parous (Tables 1 and 2). A total of 14.3% (n=672) participants developed GDM. Compared with individuals without GDM, those with GDM were older, had higher parity, had higher BMI, had higher OGTT values (including z-score) and hemoglobin A1c, had a higher mean arterial pressure, more frequently had family histories of hypertension and diabetes, and were more likely to have delivered by cesarean (P<.05 for all, Table 1). Participant characteristics similarly varied by OGTT z-score (Table 2).
Table 1.
Baseline Pregnancy Characteristics of the HAPO FUS (Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study) Cohort in the Early Third Trimester by Gestational Diabetes Mellitus Status (N=4,697)*
| Characteristic | Overall | GDM | |
| No (n=4,025) | Yes (n=672) | ||
| Age (y)† | 30.5 (26.0, 34.1) | 30.2 (25.7, 33.8) | 32.1 (28.4, 35.6) |
| Parity (any prior delivery at 20 wk or more)† | 2,422 (51.6) | 2,039 (50.7) | 383 (57.0) |
| Country† | |||
| Barbados | 584 (12.4) | 526 (13.1) | 58 (8.6) |
| Canada | 395 (8.4) | 349 (8.7) | 46 (6.8) |
| Hong Kong | 734 (15.6) | 646 (16.0) | 88 (13.1) |
| Israel | 460 (9.8) | 426 (10.6) | 34 (5.1) |
| Thailand | 247 (5.3) | 196 (4.9) | 51 (7.6) |
| United Kingdom | 1,221 (26.0) | 1,023 (25.4) | 198 (29.5) |
| United States | 1,056 (22.5) | 859 (21.3) | 197 (29.3) |
| BMI (kg/m2)† | 26.6 (24.1, 29.9) | 26.2 (23.8, 29.4) | 28.8 (26.2, 32.2) |
| Mean arterial pressure (mm Hg)† | 80.2 (74.8, 85.7) | 79.7 (74.3, 85.0) | 83.2 (78.1, 87.9) |
| Pregnancy OGTT z-score†,‡ | −0.2 (−1.6, 1.4) | −0.6 (−1.9, 0.6) | 3.4 (2.2, 4.6) |
| Pregnancy OGTT results | |||
| Fasting glucose (mg/dL) | 81.0 (77.4, 84.6) | 79.2 (75.6, 84.6) | 91.8 (82.8, 93.6) |
| 1-h glucose (mg/dL) | 131.4 (111.6, 153.0) | 127.8 (108.0, 145.8) | 180.0 (154.8, 191.3) |
| 2-h glucose (mg/dL) | 108.0 (95.4, 124.2) | 106.2 (93.6, 118.8) | 136.8 (117.0, 156.6) |
| Pregnancy Hb A1c (%) (n=4,299)† | 4.8 (4.6, 5.0) | 4.8 (4.5, 5.0) | 5.0 (4.8, 5.3) |
| Prenatal alcohol use | 402 (8.6) | 346 (8.6) | 56 (8.3) |
| Prenatal tobacco use | 243 (5.2) | 201 (5.0) | 42 (6.2) |
| Family history of diabetes† | 1,057 (22.5) | 854 (21.2) | 203 (30.2) |
| Family history of hypertension† | 1,802 (38.4) | 1,515 (37.6) | 287 (42.7) |
| Gestational age at delivery (wk) | 39.9 (39.0, 40.7) | 39.9 (39.0, 40.7) | 39.7 (38.9, 40.7) |
| Mode of delivery | |||
| Vaginal | 3,640 (77.5) | 3,182 (79.1) | 458 (68.2) |
| Planned or elective cesarean | 510 (10.9) | 407 (10.1) | 103 (15.3) |
| In-labor or emergency cesarean | 547 (11.6) | 436 (10.8) | 111 (16.5) |
GDM, gestational diabetes mellitus; BMI, body mass index; OGTT, oral glucose tolerance test; Hb A1c, hemoglobin A1c.
Data are median (interquartile range) or n (%).
Groups were compared using Wilcoxon rank sum and Pearson χ2 tests.
P<.05.
Oral glucose tolerance test performed at time of mid-pregnancy enrollment.
Table 2.
Early Third Trimester Pregnancy Oral Glucose Tolerance Test Summary z-Score by Baseline Pregnancy Characteristics in the HAPO FUS (Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study) Cohort (N=4,697)*
| Characteristic | OGTT z-Score |
| Age (y)† | |
| Younger than 25 | −0.3 (−0.8, 0.1) |
| 25–30 | −0.1 (−0.6, 0.4) |
| Older than 30 | 0.1 (−0.4, 0.6) |
| Parity† | |
| 0 | −0.1 (−0.6, 0.4) |
| 1 | 0.0 (−0.5, 0.5) |
| Country† | |
| Barbados | −0.2 (−0.7, 0.3) |
| Canada | −0.2 (−0.7, 0.3) |
| Hong Kong | 0.0 (−0.5, 0.5) |
| Israel | −0.4 (−0.9, 0.1) |
| Thailand | 0.2 (−0.2, 0.7) |
| United Kingdom | 0.0 (−0.5, 0.6) |
| United States | 0.0 (−0.5, 0.6) |
| BMI (kg/m2)† | |
| Lower than 30 | −0.2 (−0.6, 0.4) |
| 30–34.9 | 0.2 (−0.2, 0.8) |
| 35 or higher | 0.3 (−0.2, 0.9) |
| Pregnancy Hb A1c (n=4,299)† | |
| Lower than 5 | −0.2 (−0.7, 0.3) |
| 5–5.5 | 0.3 (−0.2, 0.8) |
| Higher than 5.5 | 0.8 (0.2, 1.3) |
| Prenatal alcohol use | |
| No | −0.1 (−0.6, 0.5) |
| Yes | 0.0 (−0.5, 0.5) |
| Prenatal tobacco use | |
| No | −0.1 (−0.5, 0.5) |
| Yes | 0.0 (−0.5, 0.6) |
| Family history of diabetes† | |
| No | −0.1 (−0.6, 0.4) |
| Yes | 0.1 (−0.4, 0.7) |
| Family history of hypertension† | |
| No | −0.1 (−0.6, 0.4) |
| Yes | 0.0 (−0.5, 0.6) |
| Gestational age at delivery (wk)† | |
| 37 | 0.0 (−0.6, 0.5) |
| 38 | 0.1 (−0.5, 0.6) |
| 39 or more | −0.1 (−0.5, 0.5) |
| Mode of delivery† | |
| Vaginal | −0.1 (−0.6, 0.4) |
| Planned or elective cesarean | 0.2 (−0.3, 0.7) |
| In-labor or emergency cesarean | 0.1 (−0.3, 0.7) |
OGTT, oral glucose tolerance test; BMI, body mass index; Hb A1c, hemoglobin A1c.
Data are median (interquartile range).
Groups were compared using Wilcoxon rank sum test and Pearson χ2 tests.
P<.05.
At 10–14 years after delivery (median 11.6 years, interquartile range 10.8 years, 12.4 years) and at a median age of 42.1 years (interquartile range 37.6 years, 45.8 years), the median estimated 6-year risk of MASLD was 6.0% (interquartile range 3.0%, 13.0%). The estimated risk of MASLD increased linearly in a dose–response relationship with greater pregnancy dysglycemia (Fig. 1). At 10–14 years after delivery, the median estimated 6-year risk of MASLD in individuals with GDM was 10.0% (interquartile range 5.0%, 20.0%), compared with 6.0% (interquartile range 3.0%, 12.0%) in those without GDM (P<.001).
Fig. 1. Estimated risk of metabolic dysfunction–associated steatotic liver disease (MASLD) in postpartum individuals 10–14 years after delivery by pregnancy oral glucose tolerance test (OGTT) summary z-score.
Field. Pregnancy Dysglycemia and Risk of Steatotic Liver Disease. O&G Open 2025.
In adjusted analyses, individuals with higher glucose summary z-scores had a higher estimated risk of MASLD (adjusted beta coefficient 3.15% per 1 SD; 95% CI, 2.67–3.62). Those who were diagnosed with GDM also had a higher estimated risk of MASLD (adjusted beta coefficient 4.15%; 95% CI, 3.08–5.23; Table 3). An increase of 1 SD in summary z-score was associated with greater odds of being in the highest-risk MASLD tertile (aOR 1.14; 95% CI, 1.02–1.26); however, a diagnosis of GDM was not associated with greater odds of being in a higher-risk MASLD tertile (aOR 1.05; 95% CI, 0.80–1.30).
Table 3.
Association Between Pregnancy Dysglycemia and 6-Year Estimated Risk of Metabolic Dysfunction–Associated Steatotic Liver Disease in Postpartum Individuals 10–14 Years After Delivery*
| Unadjusted Beta Coefficient (95% CI) | Adjusted Beta Coefficient (95% CI)† | Unadjusted OR (95% CI) | Adjusted OR (95% CI)† | |
| MASLD estimated risk (continuous) | ||||
| OGTT summary z-score‡ | 3.33 (2.86, 3.79) | 3.15 (2.67, 3.62) | ||
| GDM | 5.07 (4.02, 6.13) | 4.15 (3.08, 5.23) | ||
| MASLD estimated odds by tertile (categorical) | ||||
| OGTT summary z-score* | ||||
| Tertile 1 (lowest MASLD risk) | 1.00 | 1.00 | ||
| Tertile 2 | 1.14 (1.05, 1.24) | 1.07 (0.98, 1.16) | ||
| Tertile 3 (highest MASLD risk) | 1.24 (1.11, 1.38) | 1.14 (1.02, 1.26) | ||
| GDM | ||||
| Tertile 1 (lowest MASLD risk) | 1.00 | 1.00 | ||
| Tertile 2 | 1.04 (0.86, 1.26) | 0.96 (0.76, 1.16) | ||
| Tertile 3 (highest MASLD risk) | 1.18 (0.93, 1.51) | 1.05 (0.80, 1.30) |
OR, odds ratio; MASLD, metabolic dysfunction–associated steatotic liver disease; OGTT, oral glucose tolerance test; GDM, gestational diabetes mellitus.
Bold indicates statistically significant associations (P<.05).
Linear and multinomial regression with robust error variance was used.
Model adjusted for baseline HAPO (Hyperglycemia and Adverse Pregnancy Outcome) study site, age, parity, gestational age at OGTT, and duration from enrollment to follow-up.
The results of the sensitivity analysis (a gamma regression model) were consistent with the primary analyses (Appendices 1 and 2, available online at http://links.lww.com/AOG/E348).
DISCUSSION
In the prospective HAPO FUS international cohort, greater pregnancy dysglycemia in the third trimester was associated with a higher estimated risk of postpartum MASLD measured 10–14 years after delivery. These results emphasize the ability to potentially identify a group of high-risk women with prior pregnancy dysglycemia who will be at a higher risk of MASLD in the postpartum period using cardiometabolic risk factors generally available as part of clinical care.31
The findings of the current study demonstrate a continuous and dose–response association between increasing dysglycemia late in pregnancy, measured by OGTT z-score, and future postpartum risk of MASLD, as measured by a validated risk calculator.20,21 Meta-analyses have demonstrated that GDM confers an increased lifetime risk of MASLD, with an aOR ranging from 1.50 to 2.60.16,17 GDM also has been associated with metabolic dysfunction–associated steatohepatitis, a more advanced form of liver disease.32 A systematic review found that pregnant individuals with MASLD were nearly threefold more likely to be diagnosed with GDM, demonstrating a bidirectional relationship, similar to MASLD and diabetes outside of pregnancy.33 Prior epidemiologic data have individually identified many of the cardiometabolic risk factors included in the Framingham Fatty Liver Disease Risk Function such as disorders of cholesterol metabolism, increasing age, obesity, and alcohol use as risk factors for MASLD.10,34–38 Prior data also have demonstrated that the association between GDM and future MASLD persists independently of a postpartum diabetes diagnosis.32
The current study suggests the potential value of using pregnancy dysglycemia to identify postpartum individuals who have an increased estimated risk of developing MASLD. Predictive risk for MASLD may be useful to guide clinical care and prevention strategies in the postpartum period similar to those currently used with risk predictive models for cardiovascular disease.35,39 For example, it is possible that individuals with a prior history of GDM and who are at high risk for MASLD could benefit from inclusion in established screening algorithms or undergo noninvasive testing for MASLD and be targeted for prevention or optimization of risk factors, such as diabetes, obesity, dyslipidemia, and hypertension.40,41
Given that the outcome of the current study was estimated risk, future research is needed to fully characterize the relationship between pregnancy dysglycemia and actual rates of steatotic liver disease. Additionally, the degree to which the relationship between pregnancy dysglycemia and MASLD is mediated by comorbidities such as diabetes and obesity remains to be fully characterized. Further, the influence of evidence-based postpartum efforts, including behavioral intervention and pharmacotherapy, to improve cardiometabolic health on rates of MASLD among individuals with pregnancy dysglycemia requires further investigation.
There also are important limitations to note. As discussed, the study outcome was estimated 6-year risk of MASLD at the time of follow-up, rather than the frequency of steatotic liver disease or its related complications. However, assessment of estimated risk can be a useful measure to guide postpartum clinical intervention to prevent MASLD. Additionally, individuals with hypertensive disorders of pregnancy and preterm delivery, who are at high risk for adverse cardiometabolic outcomes and may also be at higher risk for MASLD, were excluded from the HAPO FUS. However, exclusion of these higher-risk groups would likely bias toward the null. We also do not have data on how subsequent reproductive trajectories, including future pregnancies and consequent adverse pregnancy outcomes, affected the association between the index pregnancy and MASLD risk. Additionally, there are limitations to the Framingham Fatty Liver Disease Risk Function, including that it has not been studied in individuals younger than age 40 years and that it has been validated against computed tomography scans, which have high specificity but lower sensitivity for diagnosis of mild steatosis compared with diagnostic liver biopsy.42 Finally, the estimated frequency of individuals at risk for MASLD in our study may be lower than current population-based estimates43 due to the lower sensitivity of computed tomography used to validate the Framingham Fatty Liver Disease Risk Function. In addition, the HAPO cohort excluded many individuals at high risk for cardiometabolic disease, such as those with preexisting diabetes and preterm births.
Strengths of the current analysis include that the study exposure was based on the results of a standardized 75-g OGTT at 24–32 weeks of gestation. Participants and clinicians were blinded to pregnancy dysglycemia screening results; hence, participants did not receive treatment for GDM. Thus, pregnancy dysglycemia was able to be evaluated continuously in addition to categorically using a GDM diagnosis without the potential effect of GDM treatment on outcomes. Additionally, data to date do not demonstrate that treatment of GDM affects the risk of adverse maternal cardiometabolic outcomes in the postpartum period.44 Risk of MASLD was assessed prospectively 10–14 years postpartum with objective measures of cardiometabolic risk and at a time point when prevention interventions to address maternal cardiovascular health can be implemented.
In conclusion, greater pregnancy dysglycemia in the third trimester was associated with a higher estimated risk of postpartum MASLD using cardiometabolic risk factors measured 10–14 years after delivery. Whether prevention and intervention for cardiometabolic risk factors in the postpartum period affects the risk of MASLD among high-risk individuals with prior pregnancy dysglycemia requires further investigation.
Footnotes
The Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS) was conducted by the HAPO FUS investigators and was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and the American Diabetes Association. The HAPO study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the American Diabetes Association. The data from the HAPO FUS reported here were supplied by the NIDDK Central Repository. This manuscript was prepared in collaboration with some of the investigators of the HAPO FUS and does not necessarily reflect the opinions or views of the HAPO FUS, the NIDDK Central Repository, or the NIDDK.
Financial Disclosure Dr. Venkatesh was supported by the Care Innovation and Community Improvement Program at The Ohio State University. The authors did not report any potential conflicts of interest.
Presented as a poster at the SMFM 2024 Pregnancy Meeting, February 10–14, 2024, National Harbor, Maryland.
Each author has confirmed compliance with the journal's requirements for authorship.
Peer reviews and author correspondence are available at http://links.lww.com/AOG/E349.
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